code: def test(loader):
      model.eval()
  
      # Compute Mean Reciprocal Rank (MRR) and Hits@1/3/10.
      result = torch.tensor([0, 0, 0, 0], dtype=torch.float)
      for data in loader:
          data = data.to(device)
          with torch.no_grad():
              log_prob_obj, log_prob_sub = model(data)
          result += model.test(log_prob_obj, data.obj) * data.obj.size(
              0
          )
          result += model.test(log_prob_sub, data.sub) * data.sub.size(
              0
          )
      result = result / (2 * len(loader.dataset))
      return result.tolist()
  
file_path: pytorch_geometric/examples/renet.py
imports: import argparse
  import os.path
  import time
  import torch
  import torch.nn.functional
  from torch_geometric.datasets import GDELT, ICEWS18
  from torch_geometric.loader import DataLoader
  from torch_geometric.nn.models.re_net import RENet
label_desc: Use image node_id 2 for calling a global function with example usage: test(loader) and returns: result